ferrotorch-nn 0.4.1

Neural network modules for ferrotorch — layers, losses, initialization
Documentation

ferrotorch-nn

Neural network modules for ferrotorch — layers, losses, hooks, and parameter management.

What it provides

  • Module<T> trait — forward, parameters / parameters_mut, train/eval, state_dict / load_state_dict
  • #[derive(Module)] proc macro — annotate fields with #[param], #[submodule], #[skip] and parameter collection + train/eval propagation are generated for you

Layers

Category Modules
Linear Linear, LazyLinear, Bilinear, LoRALinear
Convolution Conv1d, Conv2d, Conv3d, ConvTranspose1d/2d/3d
Pooling MaxPool1d/2d/3d, AvgPool1d/2d/3d, AdaptiveAvgPool1d/2d/3d, AdaptiveMaxPool*
Normalization BatchNorm1d/2d/3d, LayerNorm, GroupNorm, RMSNorm, InstanceNorm*, LocalResponseNorm
Activation ReLU, LeakyReLU, PReLU, ELU, GELU, SiLU, Mish, Sigmoid, Tanh, Softmax, LogSoftmax, Softplus, Hardswish, Hardsigmoid
Recurrent LSTM, LSTMCell, GRU, GRUCell, RNN, RNNCell
Transformer MultiheadAttention, TransformerEncoderLayer, TransformerDecoderLayer, RotaryPositionEmbedding, KVCache, SwiGLU
Embedding Embedding, EmbeddingBag
Dropout Dropout, Dropout1d/2d/3d, AlphaDropout
Container Sequential, ModuleList, ModuleDict
Utility Flatten, Unflatten, Identity, PixelShuffle, PixelUnshuffle

Losses

CrossEntropyLoss, NLLLoss, BCELoss, BCEWithLogitsLoss, MSELoss, L1Loss, HuberLoss, SmoothL1Loss, KLDivLoss, CosineEmbeddingLoss, MarginRankingLoss, TripletMarginLoss, HingeEmbeddingLoss, MultiLabelMarginLoss, PoissonNLLLoss, GaussianNLLLoss, CTCLoss.

Initialization

init::xavier_uniform_, init::xavier_normal_, init::kaiming_uniform_, init::kaiming_normal_, init::orthogonal_, init::uniform_, init::normal_, init::constant_, init::zeros_, init::ones_, with NonLinearity enum for gain selection.

Hooks

ForwardHook, BackwardHook, ForwardPreHook, HookedModule for inspection, debugging, and gradient surgery.

Quick start

use ferrotorch_core::{Float, Tensor};
use ferrotorch_nn::{Linear, Module, ReLU, Sequential};

let model: Sequential<f32> = Sequential::new(vec![
    Box::new(Linear::new(784, 256, /* bias */ true)?),
    Box::new(ReLU::default()),
    Box::new(Linear::new(256, 10, true)?),
]);

let x = ferrotorch_core::randn(&[32, 784])?;
let out = model.forward(&x)?;
assert_eq!(out.shape(), &[32, 10]);

With #[derive(Module)]

use ferrotorch_nn::{Linear, Module, ReLU};

#[derive(Module)]
struct MLP<T: ferrotorch_core::Float> {
    #[submodule] fc1: Linear<T>,
    #[submodule] act: ReLU,
    #[submodule] fc2: Linear<T>,
}

impl<T: ferrotorch_core::Float> MLP<T> {
    fn new(d_in: usize, d_hidden: usize, d_out: usize) -> ferrotorch_core::FerrotorchResult<Self> {
        Ok(Self {
            fc1: Linear::new(d_in, d_hidden, true)?,
            act: ReLU::default(),
            fc2: Linear::new(d_hidden, d_out, true)?,
        })
    }
}

Part of ferrotorch

This crate is one component of the ferrotorch workspace. See the workspace README for full documentation.

License

MIT OR Apache-2.0